Impacts of agritech on sustainable agriculture in Sub-Saharan Africa: a quantile regression approach towards SDG 2.4

Agricultural greenhouse gas emissions on the planet threaten both food security and climate change. The United Nations is calling for food security and sustainable agriculture to end hunger by 2030. Sustainable Development Goal 2.4 addresses resilient agricultural practices to combat climate change and produce sustainable food. Resilient agricultural practices are only possible with agricultural technologies (AgriTech) that will create a digital transformation in agriculture. AgriTech can meet the increasing food demand by increasing production efficiency while increasing resource efficiency by combating problems such as climate change and water scarcity. The aim of this study is to examine the impacts of AgriTech usage on sustainable agriculture in Sub-Saharan African (SSA) countries. The analyses were conducted using panel data from 20 SSA countries between 2000 and 2022. In this study, MMQR (Method of Moments Quantile Regression) provided consistent results across quantiles in variable interactions, while GMM (Generalized Method of Moments) and KRLS (Kernel Regularized Least Squares Method) approaches were used to ensure consistency of results. The findings confirm that AgriTech (ATECH) and agricultural value added (AGRW) contribute significantly to sustainable agriculture in SSA countries. The coefficients of ATECH and AGRW variables are negative and statistically significant in all quantiles. This shows that when AgriTech use and agricultural value added increase in SSA, emissions from agriculture decrease and the environment improves. However, agricultural credits (ACRD) are insufficient to reduce agricultural emissions. Furthermore, agricultural workers (AEMP) and internet use (INT) help reduce agricultural emissions up to the 60th and 50th quantiles, while this effect disappears at higher quantile levels. These results emphasize the importance of integrating green procurement and green production technologies supported by green credits into agricultural production in order to achieve sustainable agricultural development goals in SSA. Policies that facilitate farmers’ access to agricultural green credits should be adopted in SSA societies. Infrastructure works that will increase farmers’ access to the internet should be increased. Awareness of agricultural workers on green production and sustainability should be provided to agricultural workers.

Highlights.

• The results show that agricultural technologies, agricultural growth, agricultural labor, and internet use reduce agricultural emissions in SSAcountries, while credit use increases agricultural emissions.

• AgriTech use (ATECH) and agricultural value-added (AGRW) have statistically significant negative coefficients in all quantiles, indicating that increasing AgriTech and value-added reduce agricultural greenhouse gas emissions.

• The potential of AgriTech to reduce emissions is higher in low-emission quantiles (10–30%), while the effect is relatively weaker in high-emission quantiles.

• Agricultural credits (ACRD) only provide environmental improvements in the low-emission quantile (25%) and are insufficient to reduce emissions in high quantiles.

• Agricultural labor (AEMP) and internet use (INT) significantly reduced emissions at 10–50% quantiles, while this effect disappeared at higher quantiles. Farmers’ success in reducing emissions is directly dependent on their internet access.

• Panel instantaneous momentum quantile regression (MMQR) was preferred to capture heterogeneous interactions, and the robustness of the results was confirmed with the GMM and KRLS approaches.